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together to answer some of the most important questions about the causes, prevention and treatment of disease. We are seeking a Researcher to apply statistical and computational approaches towards
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machine learning methods that translate experimental measurements of compound retention and metabolism in bacteria into scalable computational models for drug discovery. The Research Assistant/Associate
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. We seek to apply innovative methods to address real-world challenges whilst delivering significant academic contributions. The post is full-time and fixed-term for 24 months. You will be based in
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information sciences. In parallel with basic research, we develop ideas and technologies further into innovations and services. We are experts in systems science; we develop integrated solutions from care
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of Aeronautics, collaborating with experts in DNS/LES, dynamical systems, and data‑driven modelling. Hands‑on experience with high‑performance computing, advanced numerical methods, and ensemble‑based optimisation
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computational approaches towards a new project which seeks to better understand the causes and consequences of the correlation between multiple traits and to quantify trait variation that arises from new
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research group of Prof Emily Shuckburgh, as well as being part of the Centre for Landscape Regeneration (CLR). The CLR is an ambitious programme of research that aims to provide the knowledge and tools
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Trust-funded project “European Tenement Biographies and the Long-Term Success of Housing Design”, led by Dr Katharina Borsi (University of Nottingham) and Prof Florian Urban (Glasgow School of Art). The
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About the Role Are you interested in how the digital revolution is changing the way children read? The InTRACt Project, led by Dr Pablo Bernabeu and Prof Victoria Murphy, is seeking a motivated
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researcher? Do you want to be trained in cutting-edge experimental and computational techniques to understand how clot contraction influences thrombus stability and embolism risk? Are you excited to be part of